Sql Web Server Business Intelligence Advancement Workshop 2014

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Sql Web Server Business Intelligence Advancement Workshop 2014 – Power BI, more than any other Microsoft product that I can remember, offers more choices and options for designing and delivering a solution. With no compromises, Power BI can be used effectively to do anything from creating a simple chart with an Excel spreadsheet to reporting and business analysis on a massive Fortune 100 data warehouse. At the end of In this post, I’ll share a comprehensive list of resources and insights from Matthew Roche, Program Director on the Power BI Customer Advisory Team (CAT). To showcase this series, I’ll start with this quote from Matthews’ blog:

Succeeding with a tool like Power BI is easy: Self-service BI tools enable more users to do more with data more easily and can help reduce the reporting burden on IT teams. Succeeding at scale with a tool like Power BI is not easy. It is very difficult, not because of the technology, but because of the context in which the technology is used. Organizations are adopting self-service BI tools because their existing approaches to working with data are no longer successful, and because the cost and pain of change have been outweighed by the cost and pain of staying the course. Matthew Roche, Building a data culture – BI Polar (ssbipolar.com)

Sql Web Server Business Intelligence Advancement Workshop 2014

When should you use dataflows versus regular Power Query? I didn’t jump on the dataflow bandwagon and struggled to adopt them at first. Frankly, Power Query is easier to use. The browser-based dataflow designer is pretty impressive, but it’s not as responsive and comfortable as the desktop app, so that’s a bit of a trade-off. The power and value of data flows becomes apparent when the company reaches a certain stage of data culture maturity.

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Before we can address the question of whether to use Power BI dataflows, conventional Power BI queries, or any other approach to get and transform data; we need to briefly review the different options for orchestrating a Business Intelligence solution in the Microsoft cloud ecosystem.

On a scale of one to ten, ten being the most formalized, strictly governed and complex corporate information platform; the Power BI self-service option can range from one to four.

For the self-service data analyst, who works entirely in Power BI Desktop, data can be imported and transformed using Power Query. Tables are modeled, calculations are defined, and data is visualized. This mode is simple and works well for small to moderate scale solutions with less emphasis on data governance and centralized control.

Even using this simple approach, data models can be developed separately from reports, certified, and shared with multiple report developers and self-service report authors. So, to some extent, business data can be managed and governed, but Power BI solution queries are read directly from source systems or files that are not selected for analytical reporting.

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The “single version of the truth” or “gold disk” repository, a data warehouse (or a smaller scale “data mart”) is the ideal solution for storing and managing reliable corporate information. The challenge of creating a central data warehouse to manage centrally governed organizational data is that it is expensive and time-consuming, but the trade-off is that self-service data models can be inaccurate and outdated. When business leaders need answers quickly, it’s not always feasible to add more data sources to a data warehouse quickly.

On the complexity scale of one to ten, versions of this option can be seven to ten.

A conventional DW/BI solution typically uses on-premise data transformation tools such as SSIS to organize and transform source data in a central data warehouse built with a relational database product such as SQL Server. While viable for on-premises systems, this old-school architecture model does not include scalable and cost-effective cloud technologies.

The first generation of Microsoft’s modern cloud-based data warehouse can use several different Azure services. The components in the following example can easily be equated to the conventional data warehouse solution in the previous example. Azure Data Lake services as a test environment that typically uses text files and structured file storage as an inexpensive landing area for ingesting source data. Azure Data Factory is used to orchestrate and transform files and data streams into and out of the data lake and data warehouse. Depending on the need for scale and size, Azure SQL Database or Azure Data Warehouse (now called Azure Synapse) can be used for data warehouse storage.

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If your organization has a comprehensive data warehouse to provide all or most of the data needed for analytical reporting, this is probably the best solution for a Power BI solution in your business environment.

Building an enterprise data warehouse solution is not a trivial endeavor, often involving as much effort in negotiating business process challenges as it does in developing the technology to implement the solution.

The next generation of Azure’s modern data warehouse is the best collection of tightly integrated cloud services called Azure Synapse Analytics. Compared to the previous set of independent Azure services, Synapse Analytics provided a unified management and development interface. Apache Spark and other industry-standard technologies designed for platform-independent data science and analytics provide the open source data preparation engine. Azure Synapse is the evolution of Azure Data Warehouse, Microsoft’s read-optimized and scalable massively parallel processing (MPP) SQL database engine.

Data flows can fill an important gap between self-service data preparation and formal data warehouse solutions. If you don’t have a full data warehouse to meet your analytical reporting requirements, but need to provide more data quality control over standardized entities, embedding data flows may be the way to go.

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In their simplest form, dataflows provide reusable transformation logic (queries) that can be shared across multiple Power BI data models. Using dataflows deployed in a workspace can prevent data model developers from repeating the same transformation steps on multiple datasets. But these are more than just Power Query scripts stored in the cloud.

A long list of capabilities are enabled through the use of data streams. They can provide standard entity and integrity definitions stored in the Dataverse (formerly known as the Common Data Model) to enforce standard naming, data types, and schema compliance, among other functions.

In Premium Capacity, data stream results can persist to Azure Data Lake Gen2 storage. This essentially allows you to use data streams to create a moderately scaled data warehouse without a large investment. Entities can be linked to related entities which creates virtual joins and referential constraints. Other Premium features include DirectQuery, Computed Entities, and Incremental Update, all handled in the data stream rather than per dataset. Integrations with Azure AI, Machine Learning, and Cognitive Services let you use AI features without writing code. For example, in a recent project, we used AutoML on a data stream containing high school student data to predict graduation outcomes.

Dataflows start with an M query, just like Power BI Desktop queries before adding the additional capabilities mentioned above. Queries are built entirely in the browser, but migrating from Power Query to Power BI Desktop is fairly easy. Start with a Power BI solution (PBIX file) on your desktop and open a query in the Advanced Query Editor. You can create a new data flow in the browser and then copy and paste the existing query M-code from the desktop into the data flow designer. You have to copy each query one at a time and there are only a few compatibility differences, but for the most part it should be a one-to-one transfer.

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Matthew Roche from the Power BI Customer Advisory team has an excellent 17-part blog series on building a data culture. Data flows are at the intersection of business processes, data management, and technology. Industry has been throwing technology and software at data governance and quality issues for decades, with marginal success. It is much easier for data professionals to recognize that these are multifaceted business culture challenges than it is to formulate a plan for success. If anyone can carry and deliver this message effectively, it’s Matthew. In this video series, he provides prescriptive guidance for recruiting an executive sponsor, working with business stakeholders, and navigating the landmines of the business landscape toward a successful data culture transition.

Honestly, I’ve only been following this series in bits and pieces for the past year and now that I’ve caught the vision, I intend to watch the entire series from start to finish. It’s that good. Think of it as Game Of Thrones with data.

Matthew also provides a comprehensive list of Power BI Dataflows resources here. Matthew recently presented to our 3Cloud Power BI and Analytics development team on using data streams to promote a data culture. This presentation was an epiphany for me, helping to better understand how data flows fit into the BI solution puzzle; that’s when the gauge metaphor came to me. I encourage you to watch it and you may have a similar moment of reckoning.

The Power BI Adoption Framework is a set of presentations from Microsoft that can serve as a checklist of important tasks and areas to cover in any Power BI implementation, large or small. These packages are also a great tool to adopt and share your organization’s BI and analytics strategy with business leaders and stakeholders. You can use

Pdf) Automating Component Dependency Analysis For Enterprise Business Intelligence

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